Modern and next-generation networks have very complex architectures with very large scales. For example, a typical nationwide cellular network consists of around 75000 eNodeBs. This number is expected to grow several-folds with the advent of 5G. Managing and operating such large network without an impact on customer service-quality is challenging from an operational viewpoint.
Network operators strive to maintain high operational efficiency while maintaining acceptable levels of customer service-quality. The network operators must strive to manage networks of increasingly larger size without proportional increases in operational expense. Managing network operations manually is becoming increasingly complex, a challenge that is exacerbated by the large number of inconsistent and incompatible hardware and software systems and devices in the enterprise. Furthermore, troubleshooting network, client, or application issues is a complex, end-to-end problem that can often involve over a hundred points of failure between the user and the application
Currently existing systems for network operations are based on two kinds of capabilities. The first is the use of monitoring tools. These tools help monitor different metrics reflecting customer experience from the network. These metrics are either raw metrics or derived metrics like key performance indicators, anomalies with respect to baseline trends etc. The second is the use of reactive tools. These tools allow operations personnel to process different network measurements at or after the failure event and perform correlations based on expert knowledge of the network. However, none of these approaches provide a solution towards predicting the risk associated with different elements so that the operation team can react based on high risk elements which have higher potential of causing service degradation and preempt the poor customer experience.
There is a need to manage a network of increasingly larger size without proportional increase in operational expenses. There is a need to pro-actively identify the network elements that are more at risk of impacting customer Quality of Experience (QoE).